Distribution System Operators have the duty of managing the distribution system safely and efficiently. Distribution revenues are generally based on asset value but revenue systems based to some extent on network performance (e.g. on the level of losses or reliability) are becoming common in many countries. Thus the distribution planner has to maximise the profit of the investments for the system development and to improve the performance of the system as well. In many case the goals are conflicting and multiobjective planning criteria may be adopted to reduce network costs and investments and to maximise some specific performance (e.g. network reliability). Furthermore, with multiobjective programming the distribution planner has the possibility of choosing a trade-off solution in a set of acceptable solutions by applying criteria to minimise risks in uncertain scenarios. These features are particularly useful whether, according to the EU directive EU/2003/54, Embedded Generation (EG) and DSM are considered as possible planning alternatives to be compared with more traditional development actions in the search for the best solution to planning problems. In this context, a methodology is presented based on multiobjective programming and decision theory which helps the decision maker to find the best development plan for the system by using the EG as a development option and by explicitly managing risks and uncertainties. In particular, the proposed methodology solves the problem of maximising the network performance by optimising some power quality indicators, like voltage quality and harmonic distortion, and by minimising the network costs (e.g. energy losses or upgrade investments) with an optimised use of EG. For this reason, the problem of the optimal sizing and siting of EG has been reformulated as a constrained, multiobjective, and nondifferentiable optimisation problem. The main idea is to apply a double trade-off procedure: the first trade-off, by means of a multiobjective ɛ-constrained technique, allows a wide family of good EG siting and sizing solutions for all the scenarios considered (e.g. associated to a different set of wind speed at all the possible locations); the second one allows isolating the most robust solutions. In this way the planner is completely free to drive the optimisation in a certain direction without loosing objectivity and generality. Simulation results obtained by using the proposed procedure on a test system demonstrate that alternatives disregarded by the decision theory application can result the most interesting ones.

The necessity for flexible electric systems, changing regulatory and economic scenarios, energy savings and environmental impact are providing impetus to the development of distributed generation, which is predicted to play an increasing role in the future electric power system; with so much new Distributed Generation (DG) being installed, it is critical that the power system impacts be assessed accurately so that DG can be applied in a manner that avoids causing degradation of power quality, reliability and control of the utility system. Considering that uncertainties on DG power production are very relevant and different scenarios have to be taken into consideration, traditional deterministic planning techniques should not be used to take the right decisions. In this paper a three step procedure, based on Genetic Algorithms and Decision Theory, is applied to establish the best distributed generation siting and sizing on an MV distribution network, considering technical constraints, like feeder capacity limits, feeder voltage profile and three-phase short circuit currents in the network nodes. In the last part of the paper the procedure is applied to an actual distribution networ

The new scenarios of malicious attack prompt for their deeper consideration and mainly when critical systems are at stake. In this framework, infrastructural systems, including power systems, represent a possible target due to the huge impact they can have on society. Malicious attacks are different in their nature from other more traditional cause of threats to power system, since they embed a strategic interaction between the attacker and the defender (characteristics that cannot be found in natural events or systemic failures). This difference has not been systematically analyzed by the existent literature. In this respect, new approaches and tools are needed. This paper presents a mixed-strategy game-theory model able to capture the strategic interactions between malicious agents that may be willing to attack power systems and the system operators, with its related bodies, that are in charge of defending them. At the game equilibrium, the different strategies of the two players, in terms of attacking/protecting the critical elements of the systems, can be obtained. The information about the attack probability to various elements can be used to assess the risk associated with each of them, and the efficiency of defense resource allocation is evidenced in terms of the corresponding risk. Reference defense plans related to the online defense action and the defense action with a time delay can be obtained according to their respective various time constraints. Moreover, risk sensitivity to the defense/attack-resource variation is also analyzed. The model is applied to a standard IEEE RTS-96 test system for illustrative purpose and, on the basis of that system, some peculiar aspects of the malicious attacks are pointed out.